{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "5345c703",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "from datetime import date\n",
    "import pandas_ta as ta\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import datetime\n",
    "import fix_yahoo_finance as yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "28bbfc31",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_smas(df):\n",
    "    df['SMA10'] = ta.sma(df[\"CRYPTO_INDEX\"], length=10)\n",
    "    df['SMA30'] = ta.sma(df[\"CRYPTO_INDEX\"], length=30)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "bea9e61b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def determine_primary_market(df):\n",
    "    df['BEAR'] = np.where(df['SMA10'] < df[\"SMA30\"], df['CRYPTO_INDEX'], np.NaN)\n",
    "    df['BULL'] = np.where(df['SMA10'] > df[\"SMA30\"], df['CRYPTO_INDEX'], np.NaN) \n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "17e8e04c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Index Calculation\n",
    "def load_crypto_data(symbols):\n",
    "    today = datetime.date.today()\n",
    "    today_str = today.strftime(\"%Y-%m-%d\")\n",
    "    #  Get last year's data\n",
    "    start_date = today - (600 * US_BUSINESS_DAY)\n",
    "    start_date_str = datetime.datetime.strftime(start_date, \"%Y-%m-%d\")\n",
    "    # Download data from Yahoo Finance\n",
    "    try:\n",
    "        df = si.get_data(symbols, start_date=start_date_str, end_date=today_str, index_as_date=False)\n",
    "        return df\n",
    "    except:\n",
    "        print('Error loading stock data for ' + symbols)\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "295d5dfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  3 of 3 completed\n"
     ]
    }
   ],
   "source": [
    "symbols = ['XLM-USD','DOGE-USD','STX-USD']\n",
    "\n",
    "start = datetime.datetime(2020,5,1)\n",
    "end = datetime.datetime(2022,4,29)\n",
    "crypto_df = yf.download(symbols, start=start, end=end)\n",
    "\n",
    "#  Convert columns from tuple to strings\n",
    "crypto_df.columns = ['{}_{}'.format(x[0], x[1]) for x in crypto_df.columns]\n",
    "column_names = ['Close_STX-USD', 'Close_DOGE-USD', 'Close_XLM-USD']  \n",
    "crypto_df['CRYPTO_INDEX']= crypto_df[column_names].sum(axis=1) / len(symbols)\n",
    "crypto_df = calculate_smas(crypto_df)\n",
    "crypto_df = determine_primary_market(crypto_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e24137bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_crypto_index(df):\n",
    "    fig = make_subplots(rows=1, cols=1)\n",
    "\n",
    "    #  Plot close price\n",
    "    #fig.add_trace(go.Line(x = df.index, y = df['CRYPTO_INDEX'], line=dict(color=\"blue\", width=1), name=\"Crypto Index\"), row = 1, col = 1)\n",
    "    \n",
    "    #  Plot SMAs\n",
    "    fig.add_trace(go.Line(x = df.index, y = df['SMA30'], line=dict(color=\"blue\", width=1), name=\"SMA30\"), row = 1, col = 1)\n",
    "    fig.add_trace(go.Line(x = df.index, y = df['SMA10'], line=dict(color=\"orange\", width=1), name=\"SM10\"), row = 1, col = 1)\n",
    "   \n",
    "    #  Plot Bull/Bear market\n",
    "    fig.add_trace(go.Line(x = df.index, y = df['BULL'], line=dict(color=\"green\", width=1), name=\"Bullish Trend\"), row = 1, col = 1)\n",
    "    fig.add_trace(go.Line(x = df.index, y = df['BEAR'], line=dict(color=\"red\", width=1), name=\"Bearish Trend\"), row = 1, col = 1)\n",
    "    \n",
    "    #  Plot Coins\n",
    "    #fig.add_trace(go.Line(x = df.index, y = df['Close_STX-USD'], line=dict(color=\"black\", width=1), name=\"STX-USD\"), row = 1, col = 1)\n",
    "    #fig.add_trace(go.Line(x = df.index, y = df['Close_DOGE-USD'], line=dict(color=\"yellow\", width=1), name=\"DOGE-USD\"), row = 1, col = 1)\n",
    "    #fig.add_trace(go.Line(x = df.index, y = df['Close_XLM-USD'], line=dict(color=\"purple\", width=1), name=\"XLM-USD\"), row = 1, col = 1)\n",
    "\n",
    "    fig.update_layout(\n",
    "        title={'text':'Primary Markets', 'x':0.5},\n",
    "        autosize=False,\n",
    "        width=800,height=400)\n",
    "    fig.update_yaxes(range=[0,1000000000],secondary_y=True)\n",
    "    fig.update_yaxes(visible=False, secondary_y=True)  #hide range slider\n",
    "    \n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "d1edd14b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dutch\\anaconda3\\envs\\tradesystem1\\lib\\site-packages\\plotly\\graph_objs\\_deprecations.py:378: DeprecationWarning:\n",
      "\n",
      "plotly.graph_objs.Line is deprecated.\n",
      "Please replace it with one of the following more specific types\n",
      "  - plotly.graph_objs.scatter.Line\n",
      "  - plotly.graph_objs.layout.shape.Line\n",
      "  - etc.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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         "line": {
          "color": "blue",
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